Trial Eligibility Agent

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---
name: trial-eligibility-agent
description: Parse trial protocols and patient data to produce criterion-level MET/NOT/UNKNOWN determinations with evidence and gaps for clinical trial screening tasks.
allowed-tools:
  - read_file
  - run_shell_command
measurable_outcome: 'Produce a MET/NOT/UNKNOWN matrix with supporting citations for ≥90% of inclusion/exclusion criteria within 5 minutes per trial request.'
---

## At-a-Glance
- **description (10-20 chars):** Trial triage hub
- **keywords:** eligibility, ClinicalTrials, FHIR, evidence, gaps
- **measurable_outcome:** Produce a MET/NOT/UNKNOWN matrix with supporting citations for ≥90% of inclusion/exclusion criteria within 5 minutes per trial request.

## Inputs
- `trial_id` (NCT or sponsor ID) plus protocol text if not public.
- `patient_summary` narrative and optional `patient_structured` FHIR bundle.
- Declare data sources used (notes, labs, imaging, meds) to show provenance.

## Outputs
1. Structured table (JSON recommended) listing each criterion id/text with status, evidence snippet, and confidence.
2. Overall recommendation (`potentially_eligible`, `not_eligible`, `needs_more_information`).
3. Data gap checklist covering missing labs/imaging/biomarkers.

## Workflow
1. **Acquire protocol:** Pull eligibility text from ClinicalTrials.gov or sponsor PDF.
2. **Normalize criteria:** Break into atomic checks with AND/OR logic and thresholds.
3. **Extract patient facts:** Map narrative + FHIR data into canonical features (age, labs, ECOG, biomarkers).
4. **Evaluate:** Assign MET/NOT/UNKNOWN with cited evidence for each criterion, flag missing context explicitly.
5. **Summarize:** Present recommendation and highlight gating unknowns plus next-best actions.
- **Oncology neuro-symbolic matching:** For oncology trial screening, extract eligibility at the criterion level, ground oncology entities and rules in an oncology-specific knowledge graph, separate agent roles for protocol parsing, patient fact extraction, rule evaluation, and conflict review, track prospective evaluation metrics without inventing unsupported benchmarks, preserve patient-level audit trails, and require human review for eligibility conflicts or uncertain criteria.

## Guardrails
- Never claim enrollment decisions; mark outputs as advisory.
- Cite direct patient evidence for every MET/NOT call; default to UNKNOWN rather than guessing.
- Respect PHI handling expectations—avoid storing raw notes outside secure paths.

## Tooling & References
- Use `README.md` for API snippets (FHIR parsing, JSON schema) and dependency versions.
- Pair with `Clinical/Trial_Matching/TrialGPT` when retrieval/ranking is also needed.

## References
- PubMed PMID 42004487: https://pubmed.ncbi.nlm.nih.gov/42004487/


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